Copolymer sequence distribution

The full picture of the factors affecting copolymer sequence distribution and their relative importance still needs lo be filled in. 

Nuclear magnetic resonance spectroscopy of dilute polymer solutions is utilized routinely for analysis of tacticlty, of copolymer sequence distribution, and of polymerization mechanisms. The dynamics of polymer motion in dilute solution has been investigated also by protoni - and by carbon-13 NMR spectroscopy. To a lesser extent the solvent dynamics in the presence of polymer has been studied.Little systematic work has been carried out on the dynamics of both solvent and polymer in the same systan. 

C nuclear magnetic resonance spectroscopy can be employed to study changes in copolymer sequence distribution brought about by differences in monomer feed profiles. Sequence distributions characteristic of conventional, staged, and power-feed copolymers are easily distinguishable in a model system of the type described here. 

In addition to the relative ratio of the monomers, the arrangement of the units in the chain is important. This arrangement is referred to as the copolymer sequence distribution. In the previous discussion, the assumption was made that the comonomer units were well mixed in the polymer chain. If this is not the case, parts of the chain can reflect properties of the corresponding homopolymer. It is thus possible to produce polymers that have significantly different properties in different parts of the polymer chain. A most dramatic example of this can be found in styrene-butadiene-styrene or styrene-isoprene-styrene thermoplastic elastomers. The properties of these unique materials will be discussed in the section Thermoplastic Elastomers. ... 

Another short essay outline some of the advantages and problems in applying, 3C NMR to the analysis of copolymer sequence distributions. 

The temperature dependence of the total interaction parameter shows that there exists an optimum condition for the composition at a given temperature (Fig. 3). Binary blends of PEO/PS and PEO/PAA are immiscible and miscible, respectively, at room temperature. The shape of curves implies that the homopol-ymer/homopolymer blends will exhibit UCST behaviors. A drastic effect of the sequence distribution on the miscibility can be found in Fig. 4. As the AA content in SAA increases from 5 mol% (Fig. 4a) to 7 mol% (Fig. 4b) to 10mol% (Fig. 4c), the blend becomes more miscible. The blend with random copolymers becomes miscible at a composition between 5 and 7 mol%, which agrees well with the experimental results [15]. At 7 mol%, the blend with block copolymers shows positive x> while the blend with random copolymers has negative y. This is very interesting because the miscibility could be controlled only by the change of copolymer sequence distributions. 

If there are more than two components in a mixture (as in a blend of a homopolymer with a copolymer), binary interaction parameters can be combined into a composite % parameter to describe the overall behavior of the system. For example, Choi and Jo [11] showed how the effects of copolymer sequence distribution in blends of polyethylene oxide) with poly(styrene-co-acrylic acid) can be described by an atomistic simulation approach to estimate the binary intermolecular interaction energies which are combined into a total interaction parameter for the blend. Their paper [11] also provides a list of the many preceding publications attempting to address the effects of copolymer composition, tacticity, and copolymer sequence distribution on polymer blend miscibility. In addition to the advances in computational hardware and software which have made atomistic simulations much faster and hence more accessible, work in recent years has significantly improved the accuracy of the force fields [12] used in such simulations. 

Controlled variables affecting product quality. The most important of these are MM, MMD, monomer conversion, copolymer composition distribution, copolymer sequence distribution and degree of branching. Those which can be measured should be controlled however, most of these variables also are not measurable on-line. For those variables which are not measurable, all identifiable inputs must be controlled in order to maintain the unmeasured output at a constant value. 

The purpose of these simulations is to evaluate the importance of copolymer sequence distribution on its ability to compatibilize a biphasic interface. Thus, an understanding of the compatibilization process is necessary to decide how this ability can be analyzed. The added copolymer is thought to act as a polymeric surfactant in that it migrates to the biphasic interface of a phase separated blend and lowers the interfacial... 

Interestingly, these results may be explained by a careful examination of the sequence distributions of experimental copolymers. Due to the reactivity ratios of the monomers, the PS/PMMA random copolymer (n = 0.46, r2= 0.52) produces a copolymer with a Px value of is 1.28 and is thus alternating in nature, whereas, both PS/P2VP (rl=0.5, r2= 1.3 Px = 0.92) and PS/PE (ri=0.78, r2= 1.39, Px = 0.89) random copolymers are more blocky in nature. Thus, the blocky random copolymer strengthens the interface while the alternating-random copolymer does not. Unfortunately, in all of the experimental studies mentioned above there exist other parameters besides sequence distribution that could influence the correlation of these experimental results to the Monte Carlo work. Thus, further experiments are currently underway in our laboratory to more carefully correlate the influence of copolymer sequence distribution to its ability to modify the biphasic interface in a polymer blend. 

For copolymers, instead, the analysis of the stereosequences is often complicated by the effect of copolymer sequence distribution. In these cases, a parallel MS investigation of the copolymer sequence distribution by MS (see Section 4) may be of help. MS is insensitive to the stereosequence, but it provides independent information on the sequence of the comonomer units, and this knowledge can be used to help interpret the NMR spectra. 

Let us consider a mixture of two random copolymers of die same chemical structure. The quantities of interest are and which represent the molar fraction of A units of the first and of the second copolymer, and X the molar fraction of the first copolymer in the mixture. The theory shows that the copolymer sequence distribution followed by mixtures of two copolymers is peculiar (sequences due to the presence of both components of the... 

Contrary to MS, where the mass number associated with a given oligomer can be determined a priori, the NMR chemical shift of a dyad or triad sequence does not have a simple relationship with sequence properties. To determine tile copolymer sequence distribution by NMR one first proceeds to the assigned dyad and triad peaks (or higher sequences). The task of peak assignment is... 

Copolymer sequence distribution as well as the length and the number of blocks. 

Reactivity ratios also control copolymer sequence distribution along the chain. The probability Pi 1 that an Mi unit follows another Mi unit in the copolymer chain is given by ... 

From a control standpoint, the most important variables are those which ultimately affect the end-use properties. These will be referred to as controlled variables affecting product quality. The most important of these are MW, MWD, monomer conversion, copolymer composition distribution, copolymer sequence distribution, and degree of branching. Most of these variables are not measurable on-line. The common approach is to control those variables which are measurable, to estimate those which are estimable and control based on the estimates, and to fix those which cannot be estimated by controlling the inputs to the process. Closed-loop control involves the adjustment of some manipulated variable(s) in response to a deviation of the associated control variable from its desired value. The purpose of closed-loop control is to bring the controlled variable to its desired value and maintain it at that point. Those variables which are not controllable in a closed-loop sense are maintained at their desired values (as measured by laboratory or other off-line measurement) by controlling all the identifiable input in order to maintain an unmeasured output at a constant value. 

The purpose of this chapter, therefore, is to describe the basic concepts of the statistical analysis of copolymer sequence distribution. The necessary relationships between various comonomer sequence abundances are introduced, along with simple statistical models based on monomer addition probabilities. The relationships between the statistical models and propagation models based on reactivity ratios are discussed. The use of these models is then illustrated by means of selected examples. Techniques for extracting sequence information from in situ NMR measurements are also described. Finally, the statistical analysis of chemically modified polymers is introduced with examples. 

Equations (2.20) and (2.21) can also be used, of course, to determine comonomer addition probabilities from reactivity ratios derived by some other means. From these, copolymer sequence distribution can be predicted using expressions such as those in Table 2.3. 

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